Lesson 4. iCite

The Relative Citation Ratio (RCR) evaluates the influence of a scientific article by analyzing its co-citation network. An RCR of 1.0 represents the median citation rate for articles in a given field. Values above 1.0 indicate that an article is cited more frequently than average, suggesting greater impact or influence. In this lesson, we’ll use the NIH Reporter to identify an active project at The Ohio State University with associated publications. We will then retrieve the RCR values for the publications using the iCite API.

Data skills | concepts

• Working with APIs

Learning objectives

1. Locate API documentation and identify key components required to formulate an API request
2. Parse the API response and store extracted data.

This tutorial is designed to support multi-session workshops hosted by The Ohio State University Libraries Research Commons. It assumes you already have a basic understanding of Python, including how to iterate through lists and dictionaries to extract data using a for loop. To learn basic Python concepts visit the Python - Mastering the Basics tutorial.

LESSON 4

The NIH Reporter is a database of NIH-funded research projects that provides information about grants, contracts, publications, patents, and other outcomes resulting from NIH funding.

Step 1: Locate an active NIH project

• Go to the NIH Reporter.
• Under Fiscal Year, select Active Projects.
• Under Organization, search for and select Ohio State University.

nih_reporter_1_search.png

Step 2: Browse active projects

• As of May 6, 2025 there were 687 active projects at The Ohio State University.
• Browse the list and click on a project that interests you.

nih_reporter_2_select_project.png

Step 3: Review project details

• Read the project abstract to understand its focus.
• To check for related publications:
  • Click on Publications in the left-hand menu, or
  • Scroll to the bottom of the project page.

nih_reporter_3_select_publications.png

Step 4: Export the publications

• If the publications are listed, use the Export option to download the list for further analysis.

nih_reporter_4_export_publications.png

Use Pandas to read the exported publication file into Python.

Tip

Move the file from downloads to your project folder.

import pandas as pd
df=pd.read_csv('data/Publications_10875609_6May2025_171210.csv') #Change the path to your downloaded file

Each article indexed in PubMed has a unique PMID (PubMed Identifier). The exported file includes a PMID column. Create a list of PMIDs found in the PMID column. To prepare the data:

• Remove any missing (null) PMIDs
• Convert each PMID from a float to integer, then to string.

import pandas as pd
df=pd.read_csv('data/Publications_10875609_6May2025_171210.csv') 
pmid_list=df.PMID.dropna().tolist()
pmids=[]
for pmid in pmid_list:
  pmids.append(str(int(pmid)))
pmids

Launch iCite.

Scroll to the bottom and and review:

• Web Policies and Notices
• Bulk Data and APIs

On the Bulk Data and APIs page, locate the Python code example for querying the iCite API.

import requests


response = requests.get(
    "/".join([
        "https://icite.od.nih.gov/api",
        "pubs",
        "23456789",
    ]),
)
pub = response.json()
print(pub)

Adjust the Python code example to extract the following fields for each PMID.

• pmid
• doi
• journal
• relative citation ratio
• cited by
• references
• title
• year

import pandas as pd
import requests

results=pd.DataFrame(columns=['pmid','doi','journal','rcr','cited_by','references','title','year'])

df=pd.read_csv('Publications_10875609_6May2025_171210.csv') 
pmid_list=df.PMID.dropna().tolist()
pmids=[]
for pmid in pmid_list:
  pmids.append(str(int(pmid)))

count=1
for each_pmid in pmids:
    print(f"Starting record {count}: {each_pmid}")
    response = requests.get(
        "/".join([
        "https://icite.od.nih.gov/api",
        "pubs",
        each_pmid,
        ]),
        )   
    pub = response.json()
    citing_pmids=pub['cited_by']
    cited_by=[]
    for each_citation in citing_pmids:
        cited_by.append(str(each_citation))
    reference_pmids=pub['references']
    references=[]
    for each_reference in references:
        references.append(str(each_reference))

    row = {
            'pmid': pub['pmid'],
            'doi': pub['doi'],
            'journal': pub['journal'],
            'rcr': pub['relative_citation_ratio'],
            'cited_by': ','.join(cited_by),
            'references': ','.join(references),
            'title': pub['title'],
            'year': pub['year']
            }
    each_row=pd.DataFrame(row, index=[0])
    results=pd.concat([each_row, results], axis=0, ignore_index=True)
    count += 1